Marco A. Acevedo Zamora


Project Overview

In this PhD project, we will do a geological study of a mineral deposit and its mineral processing using several microscopy techniques and images Big Data for better understanding trace element distribution. This work is special because we will use Artificial Intelligence tools (Machine Learning) for ore and gangue characterization that are promising for revolutionizing microscopic studies of thin sections (Petrography), a common ground between Industry and Academia, benefiting geological studies and the society.

We want to develop new data acquisition (1st work package, WP1) and analysis tool software (2nd work package, WP2) to elaborate empirical models for predicting trace element behavior. The WP1 is scheduled for the end the project and include the development of Ultra-fast LA-ICP-MS analysis. The WP2 include developing software to process microscopy image Big Data from state-of-the-art analytical techniques, using Image Processing and combining their pixel information at an adequate resolution for representation and spectral study using Correlation Microscopy and Artificial Intelligence.

We aim to characterize and understand trace (sub-0.1 wt. %) constituents in rock thin sections from mineral deposits and processing products. We are looking forward to for increasing resource efficiency in the mining value chain. The methodology development objectives are be divided in two work packages.

  • For the WP1, we want to achieve trace element mapping with ultra-fast LA-ICP-MS analysis. We will need to review experimental issues with multiple calibration, sulphide standards (quantitative analysis), and analytical protocols (calibration, dwell time, elemental menu, etc.) according to mineral assemblages.
  • For the WP2, we will elaborate a software solution with a logical petrographic workflow underpinned with automatic and optional image processing tools for studying thin sections photographed, mapped and analyzed using optical and SEM microscopy, microprobe and LA-ICP-MS. We aim to achieve a smooth and reliable user interaction with the processing power of a common laptop or personal computer, either offline or online from a cloud.


Acevedo, M. (2016). Emplacement and Magmatic Evolution of the Val Fredda Complex intrusions (southern Adamello Batholith, N. Italy). MSc in Geology thesis archive, 177 p., University of Geneva (UNIGE).

Acevedo, M. (2012). Internship at Compañia Minera MILPO S.A. Student reports archive, 81 p., National University of Engineering (UNI). Grade obtained: 14/20. 

Acevedo, M. (2011). Internship at Compañia Minera MARSA S.A. Student reports archive, 74 p., National University of Engineering (UNI). Grade obtained: 16/20.

Conference Presentations

Controles estructurales en el distrito minero de Tintaya. L.S. Jordán, E. Sánchez, M. Acevedo, M.C. Lázaro. XVII Congreso Peruano de Geología (13 de oct. de 2014). Resúmenes extendidos, Boletín 109-14 (Sociedad Geológica del Perú).


Peruvian perspective of the Graduate Geologist. Acevedo, M. (unpublished, 2013). 

Description: A review of the opportunities for recent graduates to work or pursue a M.Sc. overseas. I present tables with information of the universities summarizing their information (ranking, city, cost, disciplines, strengths, and prestige). Focused on Australia, Canada, and briefly on USA y France. It has been shared with members of the SEG Student Chapter at UNI, Lima.


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